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In recommender system, some feature directly affects whether an interaction would happen, making the happened interactions not necessarily indicate user preference.
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Deconfounding User Satisfaction Estimation from Response Rate Bias. In Fourteenth ACM Conference on Recommender Systems . ACM, 450–455
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It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining . ACM, 381–389
Jin Huang, Harrie Oosterhuis, and Maarten de Rijke. 2022 · 2022
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FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback. In Proceedings of the ACM Web Conference 2022 . ACM, 297–307
Jie Li, Yongli Ren, and Ke Deng. 2022 · 2022
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Cross Pairwise Ranking for Unbiased Item Recommendation. In Proceedings of the ACM Web Conference 2022 . ACM, 2370–2378
Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo, and Ruiming Tang. 2022 · 2022
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Causal Representation Learning for Out-of-Distribution Recommendation. In Proceedings of the ACM Web Conference 2022 . ACM, 3562–3571
Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, and Tat-Seng Chua. 2022b · 2022
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Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, and Guandong Xu. 2022a · 2022
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A Survey on the Fairness of Recommender Systems
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2022c · 2022
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Unbiased Sequential Recommendation with Latent Confounders. In Proceedings of the ACM Web Conference 2022 . ACM, 2195–2204
Zhenlei Wang, Shiqi Shen, Zhipeng Wang, Bo Chen, Xu Chen, and Ji-Rong Wen. 2022d · 2022
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Towards Unbiased and Robust Causal Ranking for Recommender Systems. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining . ACM, 1158–1167
Teng Xiao and Suhang Wang. 2022 · 2022
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. In Proceedings of the 32nd International Conference on Machine Learning . JMLR, 2048–2057
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015 · 2057
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